Abstract

The description of a Network Management Package (NMP) for Packet Communications Units (PCUs) in a Local Area Network (LAN) is presented. The LAN consists of a Sytek LocalNet 20, a Sytek System 2000, a Sytek System 6000, and a Concord Data Systems Token/Net. The NMP accesses and controls the local and remote PCUs on the networks. The communication interface software module is responsible for opening and closing the session between the PC and PCU ports. The NMP supports a set of operations used by a network manager to control and manage the network PCUs. The user uses the NMP to integrate each PCU and check and set its attributes. The NMP was developed to run in the Computer Engineering Research Laboratory and in the University of Arizona broadband cable plant. The NMP allows different PCUs (local or remote) to check and modify PCU's attributes while users work on their own PCUs. (Abstract shortened with permission of author.)

Switches are a critical component in any networked FTI data acquisition system in order to allow the forwarding of data from the DAU to the target destination devices such as the network recorder, PCM gateways, or ground station. Commercial off the shelf switches cannot meet the harsh operating conditions of FTI. This paper describes a hardware implementation of a crossbar switching architecture that meets the reliability and performance requirements of FTI equipment. Moreover, by combining the crossbar architecture with filtering techniques, the switch can be configured to achieve sophisticated forwarding operations. By way of illustration, a Gigabit network tap application is used to demonstrate the fundamental concepts of switching, forwarding, crossbar architecture, and filtering.

Online communities have become an increasingly popular channel for social interaction, enabling knowledge and opinion sharing across a board range of topics and contexts. Their viability and sustainability depends largely on contributions from community members in terms of time, resources, and knowledge. However, how individuals' knowledge contribution behavior changes over time and what network structural characteristics influence individuals' contribution behavior is not well understood. This study investigates "co-evolution" of social networks (i.e. advice network) and knowledge contribution behavior thorough a lens of social selection and social influence mechanism. This study are particularly interested in examining the dynamics of the advice network ties and the knowledge contribution behavior in the context of virtual financial communities in which people voluntarily participate to exchanges investing-related information. Unlike popular friendship-based online social networks, virtual financial communities in this study enables members to construct their own advice network by adding, maintaining, or terminating advice ties. Changes in network ties are referred to as social selection, while changes in individuals' behavior in response to the current network position are referred to as social influence. Dynamic network modeling is applied to investigate effects of social selection and influence separately and then examine the interplay between social selection and behavioral influence. Examination of such effects both separately and simultaneously requires a longitudinal data that capture dynamic changes in both the advice ties and the behavior under study.

In this dissertation, a new artificial neural network (ANN) architecture called fuzzy adaptive recurrent counterpropagation neural network (FARCNN) is presented. FARCNNs can be directly synthesized from a set of training data, making system behavioral learning extremely fast. FARCNNs can be applied directly and effectively to model both static and dynamic system behavior based on observed input/output behavioral patterns alone without need of knowing anything about the internal structure of the system under study. The FARCNN architecture is derived from the methodology of fuzzy inductive reasoning and a basic form of counterpropagation neural networks (CNNs) for efficient implementation of finite state machines. Analog signals are converted to fuzzy signals by use of a new type of fuzzy A/D converter, thereby keeping the size of the Kohonen layer of the CNN manageably small. Fuzzy inferencing is accomplished by an application-independent feedforward network trained by means of backpropagation. Global feedback is used to represent full system dynamics. The FARCNN architecture combines the advantages of the quantitative approach (neural network) with that of the qualitative approach (fuzzy logic) as an efficient autonomous system modeling methodology. It also makes the simulation of mixed quantitative and qualitative models more feasible. In simulation experiments, we shall show that FARCNNs can be applied directly and easily to different types of systems, including static continuous nonlinear systems, discrete sequential systems, and as part of large dynamic continuous nonlinear control systems, embedding the FARCNN into much larger industry-sized quantitative models, even permitting a feedback structure to be placed around the FARCNN.

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